Industrial Fault Analysis Using Probabilistic Process Models
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Solution Overview
Problem
Existing methods for fault analysis in industrial-method plants, such as painting plants, are not sufficiently reliable or efficient in identifying fault situations, determining their causes, and predicting process deviations.
Innovation Solution
A method for fault analysis that includes automatic recognition of fault situations, storage of fault data sets, determination of fault causes and relevant process values, and generation of prediction models using machine learning techniques to identify and prioritize critical process values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual fault analysis methods are used in industrial plants, then operators can identify fault situations, but the process is time-consuming and lacks reliability
Solution Approach 1:
The patent replaces manual mechanical fault analysis with an automated computer-based system that uses machine learning models and algorithms to detect, diagnose, and analyze faults automatically, eliminating human operators from the time-consuming manual analysis process while improving reliability through consistent automated decision-making
Solution Approach 2:
The fault analysis system performs self-diagnosis and self-analysis by automatically collecting data from sensors, comparing it against learned patterns from historical fault data, and generating diagnostic results without requiring external human intervention, enabling the system to serve itself in identifying and characterizing faults
2Measurement precision
If comprehensive process values are collected for fault analysis, then fault causes can be accurately determined, but the data processing complexity increases
Solution Approach 1:
The system pre-processes and stores historical process data and fault data in structured formats before actual fault analysis occurs, creating ready-to-use training datasets and reference patterns that simplify real-time fault diagnosis by having all necessary information pre-organized and accessible
Solution Approach 2:
The machine learning models extract only the most relevant features and patterns from large volumes of process data, isolating key diagnostic indicators from the overwhelming amount of raw sensor data, thereby maintaining high diagnostic accuracy while reducing the effective data complexity that needs to be processed
3Reliability
If historical fault data is stored and analyzed, then fault patterns can be recognized, but the storage and retrieval complexity increases
Solution Approach 1:
The system creates simplified digital representations or models of historical fault situations, storing essential fault patterns and characteristics rather than complete raw data sets, allowing efficient retrieval and comparison while maintaining the ability to recognize fault patterns through these compressed representations
Data Source
AI summary
In order to provide a method for anomaly and/or fault recognition in an industrial-method plant, for example a painting plant, wherein anomalies and/or fault situations are recognisable simply and reliably by means of the method, it is proposed according to the invention that the method should comprise the following:automatic generation of an anomaly and/or fault model of the industrial-method plant that comprises information on the occurrence probability of process values;automatic input of process values of the industrial-method plant during operation thereof;automatic recognition of an anomaly and/or fault situation by determining an occurrence probability by means of the anomaly and/or fault model on the basis of the process values of the industrial-method plant that have been input and by checking the occurrence probability for a limit value,